Against the backdrop of deep integration between digitalization and intelligent technologies, social media marketing paradigms are undergoing a fundamental shift from traffic-driven to value-driven approaches. This study focuses on the technical architecture and evolutionary logic of precision marketing algorithms, systematically analyzing the perception mechanisms of multi-source heterogeneous data, the reconstruction pathways of dynamic user profiles, and algorithmic innovations in recommendation systems. The research demonstrates that semantic understanding based on deep learning and association mining through graph neural networks effectively address the data sparsity and cold-start challenges faced by traditional algorithms. The introduction of knowledge graphs enables the transition of user profiles from static labels to cognitive intelligence. Additionally, the study explores the critical role of federated learning in data element circulation to tackle privacy compliance pain points in algorithmic applications. This research aims to provide theoretical support and practical pathways for the intelligent upgrading of social media marketing, fostering a dynamic balance between marketing efficiency and user experience.